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Category: geomatics Page 2 of 4

Processing Sentinel-1 SAR images using Sentinel Application Platform (SNAP)

In this tutorial, I will walk through some of the basic steps needed to process the Sentinel-1 SAR data using SNAP. For this tutorial, I am using SNAP version 7.0. You can check the version of the SNAP from Help -> About SNAP…

  1. If you do not have already, go ahead and download the SNAP

Adding and Updating attribute to a shapefile using ArcPy

In this tutorial, we will be adding the attribute to the shapefile and updating its value using ArcPy. You need to have ArcGIS installed in order to use ArcPy. This script has been tested with ArcGIS Version 10.6 but should work with most versions.

Go ahead and download the data from here. This data … CONTINUE READING

Batch zipping your shapefiles with Python

In this tutorial, we are going to write a simple script to zip your shapefiles so you can upload them to Google Earth Engine (GEE). If you are following along, refer to this post, you can see that GEE takes in certain kind of files in for it to be valid shapefile. Alternatively, you … CONTINUE READING

Converting MUTM Everest Coordinate 1830 to WGS 84 using ArcGIS: Projection and Transformation

The Map Projection System used in Nepal is the MUTM (Modified Universal Transverse Mercator). Read more about Map Projection System in Nepal here.

In this exercise, I will be using ArcGIS. Other methods will be posted in the future.

  1. Load the data in MUTM. If you don’t have data yet, you can download from

Validation of a cadastral map created using satellite imagery and automated feature extraction techniques: A case of Nepal

Estimates suggest that only 30 percent of the world’s population has access to formal land administration systems to register and protect their land rights. Surveying and mapping cadastral boundaries using traditional, field-based methods is accurate but can prove to be extremely time, cost and labor-intensive. This makes it difficult to create or update existing cadastral … CONTINUE READING

Making forest map using GLAD TCC and TCH Layers

Use the following code or follow the linkCONTINUE READING

Make a simple landcover classification map using Regression Tree

The full code for classification after obtaining sample points is here. The full code for classification with sample points generated on the fly is here.

Note: This is a very simple method for generating Landcover maps meant for showing the basic workflow for generating Landcover maps. You can use more sophisticated algorithms like CONTINUE READING

Point sampling in Google Earth Engine

In this exercise, we will try to sample a composite image of the Landsat with the landcover feature collection that we have. The full code can be found here.
The Feature Collection has the following integer assigned for ‘landcover’ column.

0 -> urban
1 -> vegetation
2 -> water

  1. We will use Landsat.simpleComposite method

Filter a Feature Collection by attribute in Google Earth Engine

The full source code for the same can be found here.

  1. If you do not have the data yet, you can download it from here. I downloaded the Admin 1 boundary, but this method is generic, so applies to any shapefile that you have.
  2. Open up the QGIS or any other GIS software

Uploading a shapefile to Google Earth Engine

  1. In Code Editor, shapefiles can be uploaded to the assets. In the code editor, on the left side panel, go to Assets.
  2. Click NEW and Table upload.shape-file-upload
  3. In the popup screen. Make sure you have the correct path for the asset. Click SELECT. Point to the directory that has your shapefile. Earth Engine

Intricacies of implementing an ITU-T X.1303 Cross-Agency Situational-Awareness Platform in Maldives, Myanmar, and the Philippines

Maldives, Myanmar, and the Philippines are vulnerable to natural disasters. Sendai Framework of Action calls for risk reduction by implementing early warning systems. A prevailing challenge is for authorities to coordinate warnings across disparate communication systems and autonomous organizations. Cross-Agency Situational-Awareness platforms and the ITU-T X.1303 Common Alerting Protocol (CAP) interoperable data standards present themselves … CONTINUE READING

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index(EVI) from Moderate Resolution … CONTINUE READING

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